In my last article, I presented a flowchart that can be useful for those trying to select the appropriate python library for a visualization task. Based on some comments from that article, I decided to use Bokeh to create waterfall charts and bullet graphs. The rest of this article shows how to use Bokeh to create these unique and useful visualizations.
Over on Kaggle, there is an interesting data set of over 130K wine reviews that have been scraped and pulled together into a single file. I thought this data set would be really useful for showing how to build an interactive visualization using Bokeh. This article will walk through how to build a Bokeh application that has good examples of many of its features. The app itself is really helpful and I had a lot of fun exploring this data set using the visuals. Additionally, this application shows the power of Bokeh and it should give you some ideas as to how you could use it in your own projects. Let’s get started by exploring the “rich, smokey flavors with a hint of oak, tea and maple” that are embedded in this data set.
In the python world, there are multiple options for visualizing your data. Because of this variety, it can be really challenging to figure out which one to use when. This article contains a sample of some of the more popular ones and illustrates how to use them to create a simple bar chart. I will create examples of plotting data with: Pandas, Seaborn, ggplot, Bokeh, pygal and Plotly.